However, current NMF-based approaches leave room for improvement in several key directions. First, since NMF is a non-convex problem, good initializations are critical to obtain good results via the standard alternating optimization approaches (similar points are made in [1]}). Good initialization approaches have been developed for somatic or nuclear calcium imaging, where simple Gaussian shape models are useful crude approximations to the elements of \(\mathbf {A}\)  [2]}, but these approaches do not apply to dendritic or axonal imaging. Second (related), it can be hard to separate weak components from noise using current NMF-based approaches. Finally, voltage imaging data does not neatly fit in the NMF framework, since voltage traces typically display both positive and negative fluctuations around the baseline resting potential.
